Unoccupied Aerial Vehicles (UAVs) have gained prominence in remote sensing applications, a.o. in individual tree detection (ITD) and management. This study explores the potential of UAV imagery in two key applications: assessing tree health using multispectral data on the one hand and evaluating the impact of different Structure-from-Motion (SfM) software packages on ITD on the other hand. In the first case study, multispectral UAV imagery was collected from an urban park in Belgium. Normalized Difference Red Edge Index (NDRE), oftenly used as a proxy of crop health, was found to be related to traditional visual tree assessments (VTAs), which highlights the potential of UAVs to complement ground surveys by offering a ‘top view’ perspective. While limitations exist in capturing within-canopy information, such as branch structure, multispectral UAV imagery can still be useful in detecting early signs of stress, presenting an objective supplement to subjective expert-driven VTAs. In the second case study, the impact of SfM software packages on ITD was investigated using existing imagery. Pix4DMapper, Agisoft Metashape and WebODM were employed to generate 3D point clouds for individual tree delineation. Pix4DMapper and Agisoft Metashape outperformed WebODM in terms of correctly classified trees, demonstrating higher recall, precision, and F-scores. WebODM exhibited a higher fraction of false negatives and false positives, and detected smaller trees on average. The findings emphasize the importance of SfM software selection in optimizing the accuracy of ITD from UAV-derived data. In conclusion, this research underscores the potential of UAVs in enhancing tree inventories, both in an urban or forest related context. The combination of multispectral imagery for tree health assessment and careful consideration of SfM software choices for ITD can contribute valuable insights to (urban) forestry management.

SUMMARY: Genome-wide association study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an open-sourced Perl-based pipeline was developed to address the challenges of rapidly processing large-scale multi-cohort GWAS studies including quality control (QC), imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing environments. RICOPILI was created as the Psychiatric Genomics Consortium pipeline for GWAS and adopted by other users. The pipeline features (i) technical and genomic QC in case-control and trio cohorts, (ii) genome-wide phasing and imputation, (iv) association analysis, (v) meta-analysis, (vi) polygenic risk scoring and (vii) replication analysis. Notably, a major differentiator from other GWAS pipelines, RICOPILI leverages on automated parallelization and cluster job management approaches for rapid production of imputed genome-wide data. A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step of the pipeline. This includes all the associated visualization plots, to allow ease of data interpretation and manuscript preparation. Simulated GWAS datasets are also packaged with the pipeline for user training tutorials and developer work. AVAILABILITY AND IMPLEMENTATION: RICOPILI has a flexible architecture to allow for ongoing development and incorporation of newer available algorithms and is adaptable to various HPC environments (QSUB, BSUB, SLURM and others). Specific links for genomic resources are either directly provided in this paper or via tutorials and external links. The central location hosting scripts and tutorials is found at this URL: https://sites.google.com/a/broadinstitute.org/RICOPILI/home. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.